Deep Learning Approaches to Risk Stratification in Acute Gastrointestinal Bleeding

项目摘要 急性胃肠道出血占220多万住院日和192亿美元的医疗费用。 每年的医疗费用。52%至55%的急性消化道出血患者不必要 住院，导致资源浪费。虽然对出现胃肠道疾病的患者进行风险分层， 出血是推荐的，风险评估评分系统在实践中并不常用， 最佳性能，可能会被错误地应用，并且不容易更新。 大多数当前的风险评分是根据出血源的位置设计的： 下胃肠道然而，出血源的位置并不总是明确的介绍。一 基于呈现症状的初始评估的风险评分（例如，呕血、黑便、便血）， 在临床实践中更相关和有用。电子健康记录可用于识别患者， 急性胃肠道出血症状，并提取临床数据，以自动计算风险评分， 提供给供应商。机器学习（使计算机具有学习能力的研究领域， 被显式编程），特别是使用神经网络的深度学习（处理 并传输信息），可以创建基于电子健康记录的模型，其性能优于临床风险 消化道出血评分，非常适合从新数据中学习。 该提案将在电子健康记录数据上使用深度学习工具，以减少不必要的 通过识别低风险患者，对急性消化道出血患者进行住院治疗。目标是1） 开发并验证准确且临床上有用的深度学习算法，用于初始风险分层上级 2）开发并验证动态深度学习工具，以随着时间的推移对风险进行建模， 3)在电子健康记录中试用性能最佳的算法。深度学习算法将被开发 使用来自两个国家的7,000名急性胃肠道出血患者的电子健康记录数据集， 耶鲁-纽黑文卫生系统的学术医院。将对单独的数据集进行验证 在马萨诸塞州波士顿的伙伴医疗中心的病人。神经网络方法将应用于 随着时间的推移更新患者数据，以评估需要输注红细胞的轨迹。最后， 一项试点研究将在电子健康记录中实施为期3个月的最佳性能算法 测试部署的可行性以及供应商和患者的可接受性。计划的课程包括深度 学习生物医学数据、风险评估和纵向分析。 这项工作有可能通过更好地对患者进行综合风险分层来节省成本 出现明显的胃肠道出血为了实现这项建议的研究和教育目标， 导师团队包括消化道出血的主要导师和深度学习的共同导师， 基于电子健康记录的临床试验预后算法的设计和实现科学。

PROJECT SUMMARY Acute gastrointestinal bleeding accounts for over 2.2 million hospital days and 19.2 billion dollars of medical charges annually. 52% to 55% of patients with acute gastrointestinal bleeding are unnecessarily hospitalized, leading to wasted resources. Although risk stratification of patients presenting with gastrointestinal bleeding is recommended, risk-assessment scoring systems are not commonly used in practice, have sub- optimal performance, may be applied incorrectly, and are not easily updated. Most current risk scores were designed for use based on the location of the bleeding source: upper or lower gastrointestinal tract. However, the location of the bleeding source is not always clear at presentation. A risk score that bases initial assessment on presenting symptoms (e.g., hematemesis, melena, hematochezia) is more relevant and useful in clinical practice. The electronic health record can be used to identify patients with acute gastrointestinal bleeding symptoms and extract clinical data to automatically calculate risk scores that are made available to providers. Machine learning (field of study that gives computers the ability to learn without being explicitly programmed), particularly deep learning using neural networks (collection of nodes that process and transmit information), can create electronic health record-based models that perform better than clinical risk scores for gastrointestinal bleeding and are well-suited for learning from new data. This proposal will use deep learning tools on electronic health record data to decrease unnecessary hospitalization in patients with acute gastrointestinal bleeding by identifying low risk patients. The goals are to 1) Develop and validate an accurate and clinically useful deep learning algorithm for initial risk stratification superior to existing clinical risk scores 2) Develop and validate a dynamic deep learning tool to model risk over time, and 3) Pilot the best performing algorithms in the electronic health record. Deep learning algorithms will be developed using a dataset of electronic health record data of 7,000 patients with acute gastrointestinal bleeding from two academic hospitals in the Yale-New Haven Health System. Validation will be performed on a separate dataset of patients at Partners Healthcare in Boston, Massachusetts. Neural network approaches will be applied to patients’ data updated over time to evaluate the trajectory towards requiring transfusion of red blood cells. Finally, a pilot study will implement the best-performing algorithms in the electronic health record for a 3-month period to test feasibility of deployment and acceptability to providers and patients. Planned coursework includes deep learning with biomedical data, risk assessment and longitudinal analysis. This work has potential to generate cost savings through better integrated risk stratification of patients presenting with overt gastrointestinal bleeding. To meet the research and educational goals of this proposal, the mentorship team includes a primary mentor in gastrointestinal bleeding and co-mentors in deep learning, electronic health record-based clinical trial design of prognostic algorithms, and implementation science.